Most parents chart their new-borns’ growth in length and weight, now Brisbane researchers have developed a growth chart for young brains.
Researchers at QIMR Berghofer, in the inner-city suburb of Herston, have developed a computer-based growth chart that could potentially transform the way paediatricians monitor child brain health and allow for earlier identification of neurodevelopmental delays.
In collaboration with researchers and clinicians from around Australia and in Finland, a team at the medial research institute designed a non-invasive AI application to chart a child’s brain age by monitoring their brain signals while asleep.
Dr Nathan Stevenson said the team used an electroencephalogram (EEG) to measure electrical activity in the brain and developed the chart by applying machine learning algorithms to data from 2000 children from Finland and Australia.
Dr Stevenson said the tool could help clinicians identify neurological problems earlier, allowing for more effective therapeutic interventions and personalised management.
“Developmental delays affect the health of children and hinder their ability to reach their full potential,” he said.
“The European Brain Council and World Health Organisation acknowledge the need for better measures of early brain development. Our brain growth chart is one such measure.
“We have mirrored the widespread use of physical growth charts, to create a neurodevelopmental growth chart which facilitates rapid and easy clinical assessment of early-life brain maturation and health.”
Co-developer Dr Kartik Iyer said more than 10 per cent of children worldwide had a clinically relevant neurodevelopmental delay.
Dr Iyer said their tool charts a child’s neurodevelopmental age against their true birth age, to track brain health.
He said the team last year applied similar AI technology to the electrocardiogram (ECG) heart monitoring data of pre term babies to give paediatricians better information about development, but the brain age tool took the technology to a new level.
“By extracting precise information from the EEG signal, we can predict brain age and measure how this differs from a child’s actual age. If brain age is lagging for example, this can help facilitate a conversation between a clinician and a child’s caregiver on their neurodevelopmental progress,” he said.
